We note that our model belongs to a class of deep neural networks known as fully convolutional neural networks. The main advantage of using such models, which do not include dense (fully-connected) layers, is that the input samples do not have to be of the same size. This flexibilty enables a broad range of applications such as image segmentation [1]}, object tracking [2]}, crowd detection [3]}, time series classification [4]} and single-image super-resolution [5]}, [6]}, [7]}, [8]}. Different from other fully convolutional neural networks [5]}, [6]}, [7]}, [8]}, [1]}, [2]}, [3]}, [4]}, our network is specifically designed for SISR, having a custom architecture that includes an upscaling layer [17]} useful only for SISR, as well as a novel loss function.
